Elite Contribution Based Two-Stage Dynamic Grouping Algorithm for Large-Scale Optimization Problem
The co-evolution framework is an effective method for solving large-scale global optimization problems.Designing a reasonable decision variable grouping method is key to improving the performance of co-evolution algorithm.Using elite decision variables to dynamically construct elite subcomponents can improve evolutionary efficiency.This paper focuses on the characteristics of inseparable variables in large-scale optimization problems that are difficult to divide.The existing strategy may assign unrelated variables to the same subcomponents of the grouping problem.To address this issue,this paper proposes the Elite Contribution based Two-Stage Dynamic Grouping algorithm(EC-TSDG).First,the variables are randomly grouped in the pre-grouping stage.Subsequently,the contributions of variables are evaluated,and the elite contribution variables are obtained from several variable contributions.Second,in the post-grouping stage,the correlation among the variables is used to determine the remaining variables that interact with the elite decision variables and to merge them to form the elite subcomponent.This enables the variables within the elite subcomponent to correlate in pairs so as to improve the accuracy of variable grouping and convergence speed of the algorithm and to avoid correlation interference between the subcomponents.Finally,an adaptive differential evolution algorithm with an external archive is used as the optimizer for each subcomponent.Compared with other advanced algorithms on the CEC'2013 test set,the proposed algorithm exhibits a faster convergence speed than comparative algorithms.Experimental result show that the Friedman test value of EC-TSDG is 1.43,and its average ranking is 36.78%higher than that of the comparative dynamic grouping algorithm,DCC.
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